Gérard Biau
How is Artificial Intelligence Changing Science?
Research in the Era of Learning Algorithms
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Prof. Gérard Biau
Cooperation Partner
Director of Sorbonne Center for Artificial Intelligence (SCAI)
Sorbonne University
Boîte 158, tour 15-25, bureau 219
4, place Jussieu
75005 Paris
France
Tel: +(33) 1 44 27 85 63
Mail: gerard.biau@sorbonne-universite.fr
Web: https://perso.lpsm.paris/~biau/
Education and Research Experience
- 1994-95: M.A. in pure mathematics, Université Montpellier 2
- 1995-97: Graduated from Ecole des Mines de Paris, majoring in geostatistics and applied probability. Graduated as a civil engineer from the Ecole des Mines.
- 1997-98: DEA in biostatistics, mathematical statistics option,
Université Montpellier 2 - 2000: Agrégation in mathematics, majoring in probability and statistics
- 1998-00: Doctorate in applied mathematics, Université Montpellier 2
- Title: „Iterative methods in functional estimation and dynamical systems“
- 2001-04: Senior lecturer at Université Paris VI
- 2003: Habilitation to direct research, Université Paris VI
- Title: „Contribution à la statistique non paramétrique et ses applications“
- 2004-07: Professor at Université Montpellier 2
- 2007: Transferred to Sorbonne Université as professor
- 2012-17: Junior Member of the Institut Universitaire de France
- 2013-18: Director of the Laboratoire de Statistique Théorique et Appliquée (LSTA)
- 2018-21: Deputy Director of the Laboratoire de Probabilités, Statistique et Modélisation (LPSM)
- 2019-present: Director of the Sorbonne Center for Artificial Intelligence (SCAI)
Relevant Publications
- Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests, The Annals of Statistics, Vol. 43, pp. 1716-1741
- Biau, G., Bleakley, K. and Cadre, B. (2016). The statistical performance of collaborative inference, Journal of Machine Learning Research, Vol. 17 (62), pp. 1-29
- Biau, G., Cadre, B. and Rouvière, L. (2019). Accelerated gradient boosting, Machine Learning, Vol. 108, pp. 971-992
- Biau, G., Scornet, E. and Welbl, J. (2019). Neural random forests, Sankhya A, à paraître
- Biau, G., Cadre, B., Sangnier, M. and Tanielian, U. (2019). Some theoretical properties of GANs, The Annals of Statistics, à paraître
